Fingerprint classification through self-organizing feature maps modified to treat uncertainties

Date

1996-10-01

Author

Halıcı, Uğur

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In this paper, a neural network structure based on Self Organizing Feature Maps (SOM) is proposed for fingerprint classification. In order to be able to deal with fingerprint images having distorted regions, the SOM learning and classification algorithms are modified. For this purpose, the concept of ''certainty'' is introduced and used in the modified algorithms. This fingerprint classifier together with a fingerprint identifier, constitute subsystems of an automated fingerprint identification system named HALafis.(1) Our results show that a network that is trained with a sufficiently large and representative set of samples can be used as an indexing mechanism for a fingerprint database, so that it does not need to be retained for each fingerprint added to the database.

Subject Keywords

Images, Recognition, Segmentation, Model

URI

https://hdl.handle.net/11511/47668

Journal

PROCEEDINGS OF THE IEEE

DOI

https://doi.org/10.1109/5.537114

Collections

Department of Electrical and Electronics Engineering, Article

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Citation Formats

U. Halıcı, “Fingerprint classification through self-organizing feature maps modified to treat uncertainties,” PROCEEDINGS OF THE IEEE, pp. 1497–1512, 1996, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/47668.